A Real-Time IoT and Image Processing based Weeds Classification System for Selective Herbicide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, the Internet of things (IoT) plays a vital role in various sectors, including smart cities, health care, industries, and agriculture. With the advancement of IoT and machine vision-based technologies, such a manual system can be replaced with an automated weed control system. The main objective of this research work is to develop an automatic IoT-based system that detects and identifies weeds with much high accuracy and with a low computational time. Furthermore, we proposed two simple, fast, and effective methods to discriminate broad, narrow, and little weeds. The primary building blocks of the proposed system consist of a pre-processing system followed by Circular Mean Intensities and Discrete Fourier Transform, which are applied to extract useful features from images. A threshold value is then set based on these features to distinguish different classes of weeds. The experimental results produced by the proposed algorithms achieved a classification rate of 96% using 350 images.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it